Clinical, Echocardiographic, and Biomarker Associations With Impaired Cardiorespiratory Fitness Early After HER2-Targeted Breast Cancer Therapy
Bibliographic record
Abstract
Cardiorespiratory fitness (CRF) is reduced in cancer survivors and predicts cardiovascular disease (CVD)–related and all-cause mortality. However, routine measurement of CRF is not always feasible. The purpose of this study was to identify clinical, cardiac biomarker, and imaging measures associated with reduced peak oxygen consumption (VO2peak) (measure of CRF) early post–breast cancer therapy to help inform CVD risk. Consecutive women with early-stage HER2+ breast cancer receiving anthracyclines and trastuzumab were recruited prospectively. Within 6 ± 2 weeks of trastuzumab completion, we collected clinical information, systolic/diastolic echocardiographic measures, high-sensitivity troponin I, B-type natriuretic peptide, and VO2peak using a cycle ergometer. Regression models were used to examine the association between VO2peak and clinical, imaging, and cardiac biomarkers individually and in combination. Among 147 patients (age 52.2 ± 9.3 years), the mean VO2peak was 19.1 ± 5.0 mL O2·kg−1·min−1 (84.2% ± 18.7% of predicted); 44% had a VO2peak below threshold for functional independence (<18 mL O2·kg−1·min−1). In multivariable analysis, absolute global longitudinal strain (GLS) (β = 0.58; P = 0.007), age per 10 years (β: -1.61; P = 0.001), and E/e’ (measure of diastolic filling pressures) (β = -0.45; P = 0.038) were associated with VO2peak. GLS added incremental value in explaining the variability in VO2peak. The combination of age ≥50 years, E/e’ ≥7.8, and GLS <18% identified a high probability (85.7%) of compromised functional independence, whereas age <50 years, E/e’ <7.8, and GLS ≥18% identified a low probability (0%). High-sensitivity troponin I and B-type natriuretic peptide were not associated with VO2peak. Readily available clinical measures were associated with VO2peak early post–breast cancer therapy. A combination of these parameters had good discrimination to identify patients with compromised functional independence and potentially increased future CVD risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".